From facial recognition to autonomous vehicles, computer vision evolves every year. As part of artificial intelligence, it processes and analyzes visual data, helping us to automate tasks. But, even the most elaborate computer vision model wouldn’t work without precise data annotation.
Data annotation is the foundation of computer vision training, contributing to its success. It provides visual examples that the model recognizes and interprets. High-quality data annotation allows computer vision models to function in a wide range of scenarios, overcoming complexities of visual data. This prepares them to function effectively in diverse environments.
Let's explore the most common types and functions of computer vision and the role data annotation plays in the whole training process.
Core Functions of Computer Vision
While processing the visual data around us, computer vision performs specific tasks. Based on the initial training, computer vision can do the following:
- Image recognition. It identifies objects and people on the images and videos.
- Facial recognition. It can verify identity based on facial features.
- Object detection. It can understand the size and position of an object on the image.
- Semantic segmentation. It classifies segments of the image into categories (e.g., people, sceneries).
- Image categorization. It categorizes the entire image into classes based on its content.
To master computer vision to the fullest extent, you will need to pass through data annotation first. This computer vision service can be done by automatic tools or with help of in-house experts, such as those at Label Your Data.
Annotating Data for Computer Vision
Data annotation helps to label raw data, which is crucial for training machine learning (ML) algorithms. Annotated data acts as a guide, helping algorithms recognize patterns and characteristics that define various classes of objects, actions, or scenes. The more specific the data annotation, the better performance and reliability of computer vision applications.
For computer vision, these types of data annotation are the most popular ones:
- Object detection. This type of annotation identifies and specifies the location of objects on the frame, with usage of specific rectangular markups.
- 2D Boxes (bounding boxes). This type supposes drawing 2-dimensional frames around objects. It allows classifying the objects of interest into predefined categories.
- Polygons. Outlines the object on the image from multiple sides, giving precision on the shape of the object.
- 3D cuboids. Draws 3D boxes around objects, adding a 3D dimension. They help to identify the position of an object, showing height, width, and depth.
- LiDAR/RADAR. Also called sensor fusion, this type of annotation labels objects based on the 3D point cloud data that we receive from various sensors.
- Video annotation. Much like object detection on the image, this type of annotation does the same with the video, breaking it into smaller frames. It allows linking the object’s position over different frames.
- Optical Character Recognition (OCR). Transforms texts from the images to the regular text for further machine learning processing. Common for photocopies or scanned documents.
Best Practices of Making Data Annotation Accurate
Thanks to meticulous annotation, AI-based models can understand and interpret visual data precisely. The team of experts at Label Your Data stick to the following principles that ensure the final labeled datasets meet the initial requirements:
- Sticking to the guidelines. Before starting any annotation project, it’s important to understand the initial guidelines. They may include examples of already annotated data or description of tasks that future machine learning models will accomplish.
- Training of annotators. The annotators receive the needed training with guidelines and demo sessions. They agree on consistency and any other metrics for evaluation.
- Providing a pilot version. A small piece of the project can be done as a pilot to compare with initial guidelines and correct if needed.
- Organizing multiple quality checks. To deliver accurate annotation, QA happens multiple times during the whole project. We usually ensure quality after every task or milestone and also at the end of the project before submission.
By implementing these techniques, we can significantly improve the precision of human data annotation, thereby enhancing the performance of the machine learning models. As a result, the models will perform better, and we’ll reduce time and resources required for model training and refinement.
Role of Data Annotation in Computer Vision Performance
Data annotation provides clear and unambiguous examples of various classes and objects in visual data. With usage of accurately labeled images or videos, AI models learn more effectively. High-quality annotations often include a wide variety of examples for each class. They cover different angles, lighting conditions, and backgrounds. This diversity in the training data helps AI models to extend their learning to new, unseen images.
Comprehensive data annotation can also help mitigate biases in AI models. It ensures that the training data includes a balanced representation of various scenarios. Think of such applications as facial recognition, where this will have an impact. This type of computer vision is the most sensitive, leading to potential unfair outcomes.
Accurate annotation is a must for complex computer vision tasks that require a deep understanding of the visual scene. Some examples include segmentation or 3D reconstruction. Detailed labeling of objects, boundaries, and relationships within the image allows AI models to develop a nuanced understanding of the scene.
As a result, they make decisions with greater confidence. This makes a difference in critical applications, such as medical image analysis or autonomous driving. This confidence in the model's interpretations can lead to success or failure.
Wrapping up
The journey to mastering computer vision lies on the precision of data annotation. The meticulous labeling of images and videos provides AI systems with the clarity and specificity needed to distinguish between different objects, features, and scenarios. That’s why it is imperative for researchers and developers across various industries to recognize the critical importance of precision annotation. And to invest in the development of high-quality annotated datasets.
The more accurate the output of the computer vision model, the more elaborate solution we’ll get in the application of AI. With detailed model training and retraining, we may expect mastering computer vision to the next level.